Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining

Habibi, Shafi; Ahmadi, Maryam; Alizadeh, Somayeh

doi:10.5539/gjhs.v7n5p304

Cited by 80 publications

(48 citation statements)

References 25 publications

(31 reference statements)

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“…The second category deals with disease prediction and diagnosis [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76]. Numerous algorithms and different approaches have been applied, such as traditional machine learning algorithms, ensemble learning approaches and association rule learning in order to achieve the best classification accuracy.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,061

530

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The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

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Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,061

530

View full text Add to dashboard Cite

show abstract

“…In the last decade, by constructing predictive models, an attempt to identify the factors that are potentially associated with the development of diabetes through data mining techniques has been made with some promising results in predicting or even capturing diabetes at its early stage [4,[7][8][9][10][11][12]. Among these techniques, the decision tree technique was widely used in the medical field in making diagnostic approaches during clinical practice [4,11,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach

Pei

Zhang

et al. 2019

Journal of Diabetes Research

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Background Diabetes mellitus is a chronic disease with a steadfast increase in prevalence. Due to the chronic course of the disease combining with devastating complications, this disorder could easily carry a financial burden. The early diagnosis of diabetes remains as one of the major challenges medical providers are facing, and the satisfactory screening tools or methods are still required, especially a population- or community-based tool. Methods This is a retrospective cross-sectional study involving 15,323 subjects who underwent the annual check-up in the Department of Family Medicine of Shengjing Hospital of China Medical University from January 2017 to June 2017. With a strict data filtration, 10,436 records from the eligible participants were utilized to develop a prediction model using the J48 decision tree algorithm. Nine variables, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work-related stress, and salty food preference, were considered. Results The accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC) value for identifying potential diabetes were 94.2%, 94.0%, 94.2%, and 94.8%, respectively. The structure of the decision tree shows that age is the most significant feature. The decision tree demonstrated that among those participants with age ≤ 49, 5497 participants (97%) of the individuals were identified as nondiabetic, while age > 49, 771 participants (50%) of the individuals were identified as nondiabetic. In the subgroup where people were 34 < age ≤ 49 and BMI ≥ 25, when with positive family history of diabetes, 89 (92%) out of 97 individuals were identified as diabetic and, when without family history of diabetes, 576 (58%) of the individuals were identified as nondiabetic. Work-related stress was identified as being associated with diabetes. In individuals with 34 < age ≤ 49 and BMI ≥ 25 and without family history of diabetes, 22 (51%) of the individuals with high work-related stress were identified as nondiabetic while 349 (88%) of the individuals with low or moderate work-related stress were identified as not having diabetes. Conclusions We proposed a classifier based on a decision tree which used nine features of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of diabetes. The classifier indicates that a decision tree analysis can be successfully applied to screen diabetes, which will support clinical practitioners for rapid diabetes identification. The model provides a means to target the prevention of diabetes which could reduce the burden on the health system through effective case management.

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“…Although the prominent interpretability of these models has made them pioneer for simple medical explanations, but the lower bias and accurate prediction of recently introduced learning algorithms, has stimulated the statistical focus on machine learning methods. The superior performance of these state-of-the-art learning techniques has been confirmed previously in many medical studies (Chao, Yu, Cheng, & Kuo, 2014;Dezfuly & Sajedi, 2015;Habibi, Ahmadi, & Alizadeh, 2015).…”

Section: Introductionmentioning

confidence: 50%

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

Safe¹,

Mahjub²,

Faradmal³

2016

GJHS

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Background: Breast cancer is the main cause of women cancer mortality. Therefore, precise prediction of patients' risk level is the major concern in therapeutic strategies. Although statistical learning algorithms are high quality risk prediction methods, but usually their better prediction quality leads to more loss of interpretability. Therefore, the aim of this study is to compare 'Model-Based Recursive Partitioning' and 'Random Survival Forest'; whether the partitioning, as the more interpretable learning technique, could be a suitable successor for forests.

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Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining

Cited by 80 publications

References 25 publications

Machine Learning and Data Mining Methods in Diabetes Research

Machine Learning and Data Mining Methods in Diabetes Research

Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

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